// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <include/args.h>
#include <include/paddleocr.h>

#include "auto_log/autolog.h"

namespace PaddleOCR {

PPOCR::PPOCR() {
    if (FLAGS_det) {
        this->detector_.reset(new DBDetector(
                                  FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
                                  FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_limit_type,
                                  FLAGS_limit_side_len, FLAGS_det_db_thresh, FLAGS_det_db_box_thresh,
                                  FLAGS_det_db_unclip_ratio, FLAGS_det_db_score_mode, FLAGS_use_dilation,
                                  FLAGS_use_tensorrt, FLAGS_precision));
    }

    if (FLAGS_cls && FLAGS_use_angle_cls) {
        this->classifier_.reset(new Classifier(
                                    FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
                                    FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_cls_thresh,
                                    FLAGS_use_tensorrt, FLAGS_precision, FLAGS_cls_batch_num));
    }
    if (FLAGS_rec) {
        this->recognizer_.reset(new CRNNRecognizer(
                                    FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
                                    FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_rec_char_dict_path,
                                    FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num,
                                    FLAGS_rec_img_h, FLAGS_rec_img_w));
    }
}

std::vector<std::vector<OCRPredictResult>>
PPOCR::ocr(std::vector<cv::Mat> img_list, bool det, bool rec, bool cls) {
    std::vector<std::vector<OCRPredictResult>> ocr_results;

    if (!det) {
        std::vector<OCRPredictResult> ocr_result;
        ocr_result.resize(img_list.size());
        if (cls && this->classifier_) {
            this->cls(img_list, ocr_result);
            for (int i = 0; i < img_list.size(); i++) {
                if (ocr_result[i].cls_label % 2 == 1 &&
                        ocr_result[i].cls_score > this->classifier_->cls_thresh) {
                    cv::rotate(img_list[i], img_list[i], 1);
                }
            }
        }
        if (rec) {
            this->rec(img_list, ocr_result);
        }
        for (int i = 0; i < ocr_result.size(); ++i) {
            std::vector<OCRPredictResult> ocr_result_tmp;
            ocr_result_tmp.push_back(ocr_result[i]);
            ocr_results.push_back(ocr_result_tmp);
        }
    } else {
        for (int i = 0; i < img_list.size(); ++i) {
            std::vector<OCRPredictResult> ocr_result =
                    this->ocr(img_list[i], true, rec, cls);
            ocr_results.push_back(ocr_result);
        }
    }
    return ocr_results;
}

std::vector<OCRPredictResult> PPOCR::ocr(cv::Mat img, bool det, bool rec,
                                         bool cls) {

    std::vector<OCRPredictResult> ocr_result;
    // det
    this->det(img, ocr_result);
    // crop image
    std::vector<cv::Mat> img_list;
    for (int j = 0; j < ocr_result.size(); j++) {
        cv::Mat crop_img;
        crop_img = Utility::GetRotateCropImage(img, ocr_result[j].box);
        img_list.push_back(crop_img);
    }
    // cls
    if (cls && this->classifier_) {
        this->cls(img_list, ocr_result);
        for (int i = 0; i < img_list.size(); i++) {
            if (ocr_result[i].cls_label % 2 == 1 &&
                    ocr_result[i].cls_score > this->classifier_->cls_thresh) {
                cv::rotate(img_list[i], img_list[i], 1);
            }
        }
    }
    // rec
    if (rec) {
        this->rec(img_list, ocr_result);
    }
    return ocr_result;
}

void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results) {
    std::vector<std::vector<std::vector<int>>> boxes;
    std::vector<double> det_times;

    this->detector_->Run(img, boxes, det_times);

    for (int i = 0; i < boxes.size(); i++) {
        OCRPredictResult res;
        res.box = boxes[i];
        ocr_results.push_back(res);
    }
    // sort boex from top to bottom, from left to right
    Utility::sorted_boxes(ocr_results);
    this->time_info_det[0] += det_times[0];
    this->time_info_det[1] += det_times[1];
    this->time_info_det[2] += det_times[2];
}

void PPOCR::rec(std::vector<cv::Mat> img_list,
                std::vector<OCRPredictResult> &ocr_results) {
    std::vector<std::string> rec_texts(img_list.size(), "");
    std::vector<float> rec_text_scores(img_list.size(), 0);
    std::vector<std::vector<float>> rec_pos_list(img_list.size());
    std::vector<std::vector<float>> rec_width_list(img_list.size());
    std::vector<double> rec_times;
    this->recognizer_->Run(img_list, rec_texts, rec_pos_list, rec_width_list, rec_text_scores, rec_times);
    // output rec results
    for (int i = 0; i < rec_texts.size(); i++) {
        ocr_results[i].text = rec_texts[i];
        ocr_results[i].poslist = rec_pos_list[i];
        ocr_results[i].widthlist = rec_width_list[i];
        ocr_results[i].score = rec_text_scores[i];
    }
    this->time_info_rec[0] += rec_times[0];
    this->time_info_rec[1] += rec_times[1];
    this->time_info_rec[2] += rec_times[2];
}

void PPOCR::cls(std::vector<cv::Mat> img_list,
                std::vector<OCRPredictResult> &ocr_results) {
    std::vector<int> cls_labels(img_list.size(), 0);
    std::vector<float> cls_scores(img_list.size(), 0);
    std::vector<double> cls_times;
    this->classifier_->Run(img_list, cls_labels, cls_scores, cls_times);
    // output cls results
    for (int i = 0; i < cls_labels.size(); i++) {
        ocr_results[i].cls_label = cls_labels[i];
        ocr_results[i].cls_score = cls_scores[i];
    }
    this->time_info_cls[0] += cls_times[0];
    this->time_info_cls[1] += cls_times[1];
    this->time_info_cls[2] += cls_times[2];
}

void PPOCR::reset_timer() {
    this->time_info_det = {0, 0, 0};
    this->time_info_rec = {0, 0, 0};
    this->time_info_cls = {0, 0, 0};
}

void PPOCR::benchmark_log(int img_num) {
    if (this->time_info_det[0] + this->time_info_det[1] + this->time_info_det[2] >
            0) {
        AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
                               FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
                               FLAGS_precision, this->time_info_det, img_num);
        autolog_det.report();
    }
    if (this->time_info_rec[0] + this->time_info_rec[1] + this->time_info_rec[2] >
            0) {
        AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
                               FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                               FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
                               this->time_info_rec, img_num);
        autolog_rec.report();
    }
    if (this->time_info_cls[0] + this->time_info_cls[1] + this->time_info_cls[2] >
            0) {
        AutoLogger autolog_cls("ocr_cls", FLAGS_use_gpu, FLAGS_use_tensorrt,
                               FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                               FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
                               this->time_info_cls, img_num);
        autolog_cls.report();
    }
}

} // namespace PaddleOCR
